Unit 8 DB 2

Using some of the techniques covered in this unit’s reading, create a marketing test for either your resume or a product you are interested in. How would you execute on it and analyze the results? Also, include what it would take to truly operationalize the process, and if you would actually do it.

In response to your peers, would you add anything to the market test or plan?

CLASSMATE 1
A marketing test is a method used by marketers to test a specific hypothesis in an experimental
environment. This involves segmenting a group into treatment and control groups, where only one
variable is changed (Venkatesan et al., 2021). It is used to determine the best possible marketing
activity. The test aims to prove causality, meaning that the marketing activity directly causes a
change in the variable (Venkatesan et al., 2021). If the marketing activity is not introduced, the
variable does not change, and no other effects are included in that change.
Apple has been facing a challenge with falling iPhone sales, their signature product, for some time
now (Sherman, 2024). One week ago, they introduced AI as a feature in their products (Carter, 2024).
It would be interesting to assess if advertising AI features could lead to improved iPhone sales.
Therefore, I will conduct my hypothetical marketing test around this topic.
I would conduct a field experiment, an experiment executed in the real world. It has the advantage
of involving a large number of participants who behave as they would in real-world scenarios, since
they are not aware they are part of an experiment (Venkatesan et al., 2021). However, there are
important downsides to be aware of. Variables cannot be controlled as rigorously as in controlled
experiments (Venkatesan et al., 2021). For example, an advertising campaign by competitors in the
timeframe of the experiment could influence the results. Nonetheless, I think it is an appropriate
method for testing different advertising strategies. Comparing different strategies at the same time
with randomized groups should reveal the better strategy regardless of externalities. It might not
show the 100% accurate increase but it shows the better strategy, which is sufficient for my case.
The experiment I would conduct involves sending two different e-mail advertisements to two
randomized groups. One group would receive a traditional iPhone advertisement (control group),
and the other group would receive an advertisement that emphasizes the AI features (treatment
group). I would try to send this advertisement to all customers I can to make the sample size as big
as possible. It is important to randomize the groups to filter out the effects of the advertisement
change and account for differences among people. Additionally, it is crucial to hold all other
variables constant, such as pricing.
At the end of the experiment, I would measure engagement by tracking metrics such as the number
of clicks on the links to the iPhone sales page and the number of iPhones sold. By comparing these
metrics between the control and treatment groups, I can identify the more effective advertising
features and emphasize them more in future campaigns.
In Apple’s case, this experiment would be an easy and inexpensive way to test a hypothesis.
However, when conducting real-world experiments, it is essential to consider the potential
consequences. Since Apple only releases one iPhone per year, this experiment could impact sales
for the entire year. Therefore, such an experiment can be seen as too risky and might be the reason
why smaller sample sizes are often utilized. While smaller samples might introduce a slight bias,
they have the advantage of being conducted before the product is released, baring lower risk.
Regards,
Jannek
References:
Carter, S. (2024, Jun 14). Apple’s Bold Entry Into The AI Arena: The Launch Of Apple Intelligence.
Forbes. Date Retrieved: 2024, Jun 17. https://www.forbes.com/sites/digitalassets/2024/06/14/apples-bold-entry-into-the-ai-arena-the-launch-of-apple-intelligence/
Sherman, N. (2024, May 3). Apple sales fall in nearly all countries. BBC. Date Retrieved: 2024, Jun
17. https://www.bbc.com/news/articles/c99zxzjqw2ko#
Rajkumar Venkatesan, Paul W. Farris, & Ronald T. Wilcox. (2021). Marketing Analytics : Essential Tools
for Data-Driven Decisions. University of Virginia Press. Chapter 6: Marketing Experiments
CLASSMATE 2
A marketing test, often referred to as an A/B test or split test, is an experimental approach used to
compare two or more variations of a marketing strategy to determine which one performs better
(Venkatesan et al., 2012). This involves dividing the target audience into different groups and
exposing each group to a different version of the marketing material. The primary goal is to identify
the most effective approach based on predefined metrics such as engagement rates, conversion
rates, and customer feedback. Marketing tests are crucial for making data-driven decisions,
optimizing marketing efforts, and ultimately enhancing the overall effectiveness of a campaign.
For this post, I’ve decided to create a marketing test focused on addressing the widely reported
quality issues of Tesla vehicles (Hawkins, 2023). This test aims to understand consumer perceptions
and identify effective strategies to improve the brand image and customer satisfaction. Drawing
from this week’s readings, particularly the insights from Venkatesan, Farris, and Wilcox (2021), I
propose a structured experiment leveraging marketing analytics to gauge the effectiveness of
different messaging and service improvements.
Experiment Design
Our experiment will utilize A/B testing, a technique extensively discussed in Chapter 6 of our
textbook, “Marketing Analytics: Essential Tools for Data-Driven Decisions” (Venkatesan, Farris, &
Wilcox, 2021). We will create two distinct marketing campaigns:
1. Campaign A: Transparency and Commitment to Quality Improvements
• This campaign will highlight Tesla’s acknowledgment of past quality issues and
showcase specific steps the company is taking to address these concerns. This
includes detailing the improvements in manufacturing processes, new quality control
measures, and customer service enhancements.
2. Campaign B: Customer Testimonials and Enhanced Warranty Program
• This campaign will feature testimonials from satisfied customers who have
experienced the improvements firsthand. Additionally, it will introduce an enhanced
warranty program, offering extended coverage and complimentary services to
reassure potential buyers.
Execution Plan
1. Target Audience Segmentation
Using consumer behavior data, we will segment our audience into current Tesla
owners, potential buyers, and individuals who have expressed concerns about
Tesla’s quality. This segmentation allows us to tailor our messaging more effectively.
2. Implementation of A/B Testing
• Each segment will be randomly divided into two groups. One group will receive
Campaign A, and the other will receive Campaign B. We will utilize online platforms,
including social media, email newsletters, and targeted ads, to deliver these
campaigns.
3. Data Collection and Metrics
• We will track KPIs such as click-through rates, engagement levels, sentiment analysis
from social media interactions, and conversion rates (e.g., requests for test drives,
new purchases). Additionally, surveys will be conducted to gather qualitative
feedback on the perceived effectiveness of each campaign.
Analysis and Insights
Upon collecting the data, we will analyze the results to determine which campaign was more
successful in changing perceptions and driving engagement. Statistical tools will be employed to
ensure the validity and reliability of our findings. Key metrics will include:
•
Engagement Rates: Comparison of click-through and interaction rates between the two
campaigns.
• Sentiment Analysis: Evaluation of customer feedback and social media sentiment.
• Conversion Rates: Measurement of tangible actions taken by consumers, such as test drive
requests or actual purchases.
Operationalization
To operationalize this process, Tesla would need to integrate marketing analytics into its
organizational culture, as emphasized in Chapter 12 of our textbook (Venkatesan, Farris, & Wilcox,
2021). This involves:
•
Building an Analytics Team: Establishing a dedicated team to oversee the design, execution,
and analysis of marketing experiments.
• Investing in Technology: Utilizing advanced analytics platforms to collect and analyze data in
real-time.
• Creating Feedback Loops: Implementing systems to quickly act on insights gained from the
experiments, ensuring continuous improvement in marketing strategies.
Feasibility and Personal Reflection
In reality, implementing such a test requires significant resources and coordination across various
departments. While I believe this approach is both feasible and beneficial for Tesla, the actual
execution would depend on the company’s commitment to transparency and willingness to invest in
long-term quality improvements. Personally, I find this exercise enlightening as it demonstrates the
practical application of marketing analytics in addressing real-world challenges. Engaging in such
experiments enhances our understanding of consumer behavior and drives more informed
decision-making processes.
•
By designing and executing this marketing test, we can gather valuable insights into consumer
perceptions and develop strategies to enhance Tesla’s brand image. This experiment not only aligns
with the principles of descriptive analytics but also underscores the importance of data-driven
decision-making in modern marketing.
I look forward to discussing this approach with all of you and hearing your thoughts on its potential
impact and implementation.
– Marlena
References
Hawkins, A. J. (2023, December 20). Tesla vehicles have serious quality issues, part 7,294,656. The
Verge. https://www.theverge.com/2023/12/20/24009232/tesla-vehicles-have-serious-quality-issuespart-7294656
Venkatesan, R., Farris, P. W., & Wilcox, R. T. (2021). Marketing analytics: Essential tools for data-driven
decisions. Darden Business Publishing, University of Virginia Press.